CARDINALITY MODELS FOR PRIVACY-SENSITIVE ASSESSMENT OF DIGITAL COMPONENT TRANSMISSION REACH

    公开(公告)号:US20240005040A1

    公开(公告)日:2024-01-04

    申请号:US17856084

    申请日:2022-07-01

    Applicant: Google LLC

    CPC classification number: G06F21/6263 G06F16/24558 G06F21/6227

    Abstract: In one aspect, there is provided a method performed by one or more computers for privacy-sensitive assessment of digital component transmission reach based on cardinalities of subset unions of a collection of user sets, the method including: receiving a request to determine a number of users that are included in a target group of users that received at least one transmission of a digital component, where the request includes a set expression defined in terms of the collection of user sets, generating an alternative representation of the set expression in terms of primitive sets, applying a cardinality model to each primitive to generate a cardinality of each primitive set as a linear combination of cardinalities of subset unions of the collection of user sets, and determining the number of users included in the target group of users based on the cardinalities of the primitive sets.

    Confusion Matrix Estimation in Distributed Computation Environments

    公开(公告)号:US20250156300A1

    公开(公告)日:2025-05-15

    申请号:US18835683

    申请日:2023-12-04

    Applicant: Google LLC

    Abstract: An example method includes: serving content to a plurality of client devices associated with a plurality of tag values; predicting, using a prediction system, a plurality of attributes respectively associated with the plurality of tag values; generating a data sketch descriptive of the plurality of predicted attributes; noising the data sketch, wherein the noised data sketch satisfies a differential privacy criterion; transmitting the noised data sketch to a reference system; and receiving, from the reference system, estimated performance data associated with the predicted attributes, wherein the estimated performance data is based on an evaluation of: reference attribute data associated with one or more of the plurality of tag values and the predicted attributes for the one or more of the plurality of tag values.

    ENCRYPTED INFORMATION RETRIEVAL
    8.
    发明申请

    公开(公告)号:US20250013774A1

    公开(公告)日:2025-01-09

    申请号:US18896152

    申请日:2024-09-25

    Applicant: Google LLC

    Abstract: Encrypted information retrieval can include generating a database that is partitioned into shards each having a shard identifier, and database entries in each shard that are partitioned into buckets having a bucket identifier. A batch of client-encrypted queries are received. The batch of client-encrypted queries are processed using a set of server-encrypted data stored in a database. The processing includes grouping the client-encrypted queries according to shard identifiers of the client-encrypted queries, executing multiple queries in the group of client-encrypted queries for the shard together in a batch execution process, and generating multiple server-encrypted results to the multiple queries in the group of client-encrypted queries. The multiple server-encrypted results for each shard are transmitted to the client device.

    Encrypted information retrieval
    9.
    发明授权

    公开(公告)号:US12135811B2

    公开(公告)日:2024-11-05

    申请号:US18008554

    申请日:2022-06-14

    Applicant: Google LLC

    Abstract: Encrypted information retrieval can include generating a database that is partitioned into shards each having a shard identifier, and database entries in each shard that are partitioned into buckets having a bucket identifier. A batch of client-encrypted queries are received. The batch of client-encrypted queries are processed using a set of server-encrypted data stored in a database. The processing includes grouping the client-encrypted queries according to shard identifiers of the client-encrypted queries, executing multiple queries in the group of client-encrypted queries for the shard together in a batch execution process, and generating multiple server-encrypted results to the multiple queries in the group of client-encrypted queries. The multiple server-encrypted results for each shard are transmitted to the client device.

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